summary.gssanova {gss}R Documentation

Assessing Smoothing Spline ANOVA Fits with Non Gaussian Responses

Description

Calculate various summaries of smoothing spline ANOVA fits with non Gaussian responses.

Usage

summary[.gssanova](obj, diagnostics=FALSE)

Arguments

obj Object of class "gssanova".
diagnostics Flag indicating if diagnostics are required.

Details

Similar to the iterated weighted least squares fitting of glm, penalized likelihood regression fit can be calculated through iterated penalized weighted least squares.

The diagnostics are based on the "pseudo" Gaussian response model behind the weighted least squares problem at convergence.

Value

summary.gssanova returns a list object of class "summary.gssanova" consisting of the following components. The entries pi, kappa, cosines, and roughness are only calculated if diagnostics=TRUE.

call Fitting call.
family Error distribution.
method Method for smoothing parameter selection.
dispersion Assumed or estimated dispersion parameter.
iter Number of performance-oriented iterations performed.
fitted Fitted values on the scale of the link.
residuals Working residuals on the link scale.
rss Residual sum of squares.
dev.resid Deviance residuals.
deviance Deviance of the fit.
dev.null Deviance of the null model.
penalty Roughness penalty associated with the fit.
pi "Percentage decomposition" of "explained variance" into model terms.
kappa Concurvity diagnostics for model terms. Virtually the square roots of variance inflation factors of a retrospective linear model.
cosines Cosine diagnostics for practical significance of model terms.
roughness Percentage decomposition of the roughness penalty penalty into model terms.

Author(s)

Chong Gu, chong@stat.purdue.edu

See Also

Fitting function gssanova and methods predict.ssanova, fitted.gssanova.